Crop seed yield modeling and prediction can act as a key approach in the precision agriculture industry, enabling the reliable assessment of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are employed to predict sesame (Sesamum indicum L.) seed yields (SSY) using agro-morphological features. Various ML models were applied, coupled with the PCA (principal component analysis) method to compare them with the original ML models, in order to evaluate the prediction efficiency. The Gaussian process regression (GPR) and radial basis function neural network (RBF-NN) models exhibited the most accurate SSY predictions, with determination coefficients, or R² values, of 0.99 and 0.91, respectfully. The root-mean-square error (RMSE) obtained using the ML models ranged between 0 and 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. The estimation of the sesame seed yield with the coupled PCA-ML models improved the performance accuracy. According to the k-fold process, we utilized the datasets with the lowest error rates to ensure the continued accuracy of the GPR and RBF models. The sensitivity analysis revealed that the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.